Why Context is Critical to AIOps

Modern applications and technologies move around and change so frequently – daily, hourly, and increasingly shorter spans of time—it’s no longer humanly possible to keep track of everything.

 “The transformation of IT operations from back-office to business catalyst isn’t about machine learning, and it isn’t about data. This transformation is about finding context-based insights that enable the IT organization to focus on and prioritize those things that are most important to the enterprise.” – Charles Araujo, Intellyx 

Murali Nemani
CMO
ScienceLogic

Businesses today are building more applications using a wider variety of technologies and platforms than ever before. And every application, device, and sensor produces incredible amounts and types of data, at a tremendous rate. This is both a gift and a curse.  

It’s a gift because as Peter Drucker is oft-quoted, “you can’t manage what you can’t measure.” But it’s also a curse, for two main reasons. First, the data produced is inconsistent across technologies and platforms. Second, and more importantly, to properly manage IT, you need to not only know what’s happening but also understand the impact it may have on your business, and why it’s happening (root cause).  

Modern applications and technologies move around and change so frequently – daily, hourly, and increasingly shorter spans of time—it’s no longer humanly possible to keep track of everything, much less understand how it all works together.  

Attempting to make sense from a jumble of inconsistent and unorganized data often leaves IT operations teams feeling as if they’re playing “Where’s Waldo.” 

 data without context

(Data without context is like trying to find Waldo)

As a quick fix, enterprises are turning to artificial intelligence (AI) and machine learning (ML). But expecting perfect outputs (actionable results) from imperfect inputs (the data) is impossible, even for intelligent machines.  

The main culprits: bad data and missing context.  

According to Gartner, data scientists spend 79% of their time collecting, cleaning, and organizing data. 

As Charles Araujo, principal analyst at Intellyx writes in this white paper, Transforming IT Ops With Machine Learning? Apply Context., “The lack of context makes it much harder to transform all that data into meaningful insights. The information that describes the data and its relationship to other data is called metadata — and it’s the secret sauce that can unleash the power of machine learning and allow it to deliver on its promise to IT operations teams.”  

Creating clean data with context doesn’t just happen. You need a game plan.  

Join us on October 31, 2018, for our upcoming webinar, ‘How to Transform IT Operations with Machine Learning? Apply Context.,’ In the webinar, we’ll discuss how to fill in the blanks and add context to your data, so it’s immediately actionable and enables you to produce results that move your business forward.